{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib.pylab import *\n",
    "from numpy import random\n",
    "from collections import OrderedDict\n",
    "import pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def figsize(width,height):\n",
    "    rcParams['figure.figsize'] = (width,height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#%matplotlib notebook\n",
    "%matplotlib inline\n",
    "\n",
    "font = {'size'   : 22}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "figsize(8, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_perf_dim_90(dat, logx=False, saveas=None, d_Int=None):\n",
    "    plotfn = semilogx if logx else plot\n",
    "    figsize(10, 5)\n",
    "    plt.rc('text', usetex=True)\n",
    "\n",
    "    dir_perf = np.mean(dat[dat[:,0]==-1,:],0)[1]\n",
    "    sub_perf = dat[dat[:,0]!=-1,:]\n",
    "    \n",
    "    if d_Int!=None:\n",
    "        dir_perf = d_Int\n",
    "    \n",
    "    print 'dir_perf: '+ str(dir_perf)\n",
    "    \n",
    "    unique_dim = sorted(list(set(sub_perf[:,0])))\n",
    "    perf_mean = [np.mean(dat[dat[:,0]==d,:],0)[1] for d in unique_dim]\n",
    "\n",
    "    axhline(dir_perf, color=(0.0,0.0,0.0), ls='-', label='baseline')\n",
    "    axhline(.75, color=(0.0,0.0,0.0), ls='--', label='90\\% accuracy')\n",
    "    plotfn(unique_dim, perf_mean, '-', mec='b', lw=3, mfc=(.7,.7,1), color=(.7,.7,1), ms=14)    \n",
    "    plotfn(sub_perf[:,0], sub_perf[:,1], 'o', mec='b', lw=3, mfc=(.7,.7,1), color=(.7,.7,1), ms=14)\n",
    "\n",
    "    for i in range(len(unique_dim)):\n",
    "        if perf_mean[i]>.75:\n",
    "            print \"d_int=\"+str(unique_dim[i]) + \", acc=\" +str( perf_mean[i] ) \n",
    "            # plotfn(unique_dim[i], perf_mean[i], 'o', mec='b', lw=3, mfc='b', ms=14)\n",
    "            break\n",
    "\n",
    "    xlabel('Subspace dim $d$')\n",
    "    ylabel('Validation accuracy')\n",
    "    tight_layout()\n",
    "    legend(loc=4)\n",
    "    if saveas:\n",
    "        savefig('%s.png' % saveas)\n",
    "        savefig('%s.pdf' % saveas)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_sub_dim_we(dat_swem, dat_cnn, perf_b=1.0, logx=True, xmin=None,ymin=None, saveas=None):\n",
    "    \n",
    "    plotfn = semilogx if logx else plot\n",
    "    leg = OrderedDict()\n",
    "    \n",
    "    def sub_dim(dat):\n",
    "        dir_perf = np.mean(dat[dat[:,0]==-1,:],0)[1]\n",
    "        sub_perf = dat[dat[:,0]!=-1,:]\n",
    "        unique_dim = sorted(list(set(sub_perf[:,0])))\n",
    "        sub_perf = sub_perf[:,1]\n",
    "        return dir_perf, sub_perf, unique_dim\n",
    "    \n",
    "    dir_perf, sub_perf, unique_dim = sub_dim(dat_swem)\n",
    "    hhd_swem = axhline(dir_perf, color='b', ls='--')\n",
    "    hh, = plotfn(unique_dim, sub_perf, 'o-', color='b',  mfc=(.7,.7,1), ms=14)\n",
    "    leg[hh] = 'SWEM'\n",
    "    \n",
    "    dir_perf, sub_perf, unique_dim = sub_dim(dat_cnn)\n",
    "    hhd_cnn = axhline(dir_perf, color='r', ls='--')\n",
    "    hh, = plotfn(unique_dim, sub_perf, 'o-', color='r', mfc=(1,.7,.7), ms=14)\n",
    "    leg[hh] = 'CNN'    \n",
    "\n",
    "    leg[hhd_swem] = 'SWEM direct'\n",
    "    leg[hhd_cnn] = 'CNN direct'\n",
    "\n",
    "    tight_layout()\n",
    "    xlabel('Subspace dim d')\n",
    "    ylabel('Accuracy')\n",
    "    ax = axis()\n",
    "    axis([xmin, ax[1], ymin, ax[3]])\n",
    "    legend(leg.keys(), leg.values())\n",
    "    #legend?\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    if saveas:\n",
    "        savefig('%s.png' % saveas)\n",
    "        savefig('%s.pdf' % saveas)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "swem_testing_agnew_we = array([\n",
    "[-1, 0.9167],[0, 0.9120],[1, 0.9176],[2, 0.9180],[5, 0.9182],[10, 0.9192]\n",
    "])\n",
    "\n",
    "swem_training_agnew_we = array([\n",
    "[-1, 1.0000],[0, 0.9831],[1, 0.9844],[2, 0.9844],[5, 0.9849],[10, 1.0000]\n",
    "])\n",
    "\n",
    "cnn_testing_agnew_we = array([\n",
    "[-1, 0.9166],[0, 0.8796],[1, 0.9118],[2, 0.9063],[5, 0.9053],[10, 0.9088]\n",
    "])\n",
    "\n",
    "cnn_training_agnew_we = array([\n",
    "[-1, 1.0000],[0, 0.9155],[1,  0.9922],[2, 1.0000],[5, 1.0000],[10, 1.0000]\n",
    "])\n",
    "\n",
    "\n",
    "swem_testing_agnews = array([\n",
    "[-1, 0.9167],[10, 0.4197],[100, 0.6818],[200, 0.7661],[500, 0.8220],[1000, 0.8512]#,[2500,0.8633],[5000,0.8712]\n",
    "])\n",
    "\n",
    "swem_training_agnews = array([\n",
    "[-1, 0.9844],[10, 0.4219],[100, 0.6847],[200, 0.7718],[500, 0.8253],[1000, 0.8541],[2500,0.8688],[5000,0.8732]\n",
    "])\n",
    "\n",
    "cnn_testing_agnews = array([\n",
    "[-1, 0.9157],[10, 0.2972],[100, 0.6026],[200, 0.7113],[500, 0.8046],[1000, 0.8487]#,[2500,0.8779],[5000,0.8900]\n",
    "])\n",
    "\n",
    "cnn_training_agnews = array([\n",
    "[-1, 1.0000],[10, 0.2812],[100, 0.6016],[200, 0.7141],[500, 0.8083],[1000, 0.8506],[2500,0.8837],[5000,0.9013]\n",
    "])\n",
    "\n",
    "swem_testing_yelp = array([\n",
    "[-1, 0.9319],[10, 0.5757],[50, 0.6583],[100,0.7033],[200, 0.7482],[500, 0.7732],[1000, 0.7846]#,[5000, 0.7927],[10000, 0.8005],[20000, 0.7987]\n",
    "])\n",
    "\n",
    "swem_training_yelp = array([\n",
    "[-1, 0.9688],[10, 0.5938],[50,0.6598],[100,0.7056],[200, 0.7461],[500, 0.7738],[1000, 0.7828],[5000, 0.7885],[10000, 0.7973],[20000, 0.8011]\n",
    "])\n",
    "\n",
    "cnn_testing_yelp = array([\n",
    "[-1, 0.9384],[10, 0.5238],[50, 0.6199],[100, 0.6663],[200, 0.7114],[500, 0.7853],[1000, 0.8331]# ,[5000, 0.8976],[10000,0.9147],[20000,0.9254]\n",
    "])\n",
    "\n",
    "cnn_training_yelp = array([\n",
    "[-1, 0.9727],[10,  0.5311],[50, 0.6196],[100, 0.6685],[200,  0.7131],[500,  0.7850],[1000, 0.8306],[5000, 0.9024],[10000,0.9208],[20000,0.9347]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+p5rBh/FeXFOxWAxz5syxdaz5Rp/e67FhwwYAElQ4nU7LXhFzKKccPRFutzvr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c12ed50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sub_dim_we(swem_testing_yelp, cnn_testing_yelp, logx=False, ymin=0.5, saveas='testing_yelp_we')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jB1K1NKCU8kGW04aTT9JaP1tA26hKHTgA/NM/yUzE+vVSmLOqvPqqbNK2dq1s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\nEBERUWv+AAi5L1DIyJrKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd0cd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sub_dim_we(swem_testing_agnews, cnn_testing_agnews, logx=False, ymin=0.5, saveas='testing_agnews_we')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dir_perf: 0.9166\n",
      "d_int=1.0, acc=0.9118\n"
     ]
    },
    {
     "data": {
      "image/png": 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/F1QY+HXn3Kh8gGhqIFWgQZU+3l9hZuOSMs65nnBbUCUgHD6seXyj12lHFy+u\nBq379km33x5vfwAAAGppdgGCi0GgOCHpU/KBqgt2F+Rrqr7UwKnCYLHSR/3htUoN9Kc/qN0aRupn\n5RcaKJlZybmwa5u7TruJpgkcOuQnZwEAACRV0wsQmNnzkvqD0lhZ+bzWQrC90XOUnHPVJkdl5fNX\nGz3XVPRxkLM62+rrtBPKYAEAgDTZ9MpZQaDacLBawax88FguI18rtlk5ra3PulXXSaVr1/yKWSEC\nVwAAkHR1A1fn3HslZczsq5Ftd9Y4ZIWZPV2/lSblFzEo168GasEGE8W6opOzgglXJTOLBqSbuk67\nuXDBT86S/ISsffvi7Q8AAEA9jYy4Tkk6IGlXZFtBtcthuWD/rhptJElmNuGcG3bOZcNZ/UH+bFeF\nj/+XJC0GixxE5SNtsvILJKypMrCR6+wEpAkAAIC0aSRw/aTWT2r6kGoHrht1j6RTzrn54HG3mfVW\naFfU+tn/I8Gx/VrNYR2sUtqq0eu0NTMCVwAAkD5N1XHdidqpjuvLL0vBAhnatUu67z6po9nCaAAA\nAJu0pXVcnXO/Hyw0UG3/mSA3FglUXgaLoBUAAKRBsyHLmKSeGvud/Ef4SCDSBAAAQBo1G7jWK1Vf\nlFR3uBfb78YNaWFh9TGBKwAASIuG67g6594v6Xhk07Bz7kSFphn5ElNLFfYhZktLPniVpFtukfbv\nj7c/AAAAjdrIAgRZrdZBNVWuiSr5Vahm5NMJkDCkCQAAgLRqOHA1s09J+pQkOeeWJQ1EFyVAOhC4\nAgCAtGo2x3VKPo8VKfLqq9JLL/n7zvmKAgAAAGmxkVSBFWZ2vH4rJM2FC6v3Ozul3U09+wAAAPHY\ndOhSq56rmb2w2fOjdV58cfX+4cPx9QMAAKAZTQeuzrlRSSfrNNvV7PnRWmZrR1zJbwUAAGnT7MpZ\nH5NfYOArkk7J13U9Lenjkr4aPB5vUR/RAhcvSq+95u/v3Svdfnu8/QEAANioZkdchyWNmdkpSXLO\nfVzSWTP7ZvD4rHw9VyREeTUBV28JCQAAgIRptqpAVtJ85HEx2BaalpRrtlNoPcpgAQCAtGs2cC1K\n6o48npE0GHncq7WBLGJ07ZpfMStE4AoAANKo2cB1QtJA5PEZSf3Ouc87574gaUhSYbOdQ2ssLPjJ\nWZLPbd23L97+AAAANKPZHNe8pJUxPDN7yjn3IUlfDDYVJFHrNSFIEwAAAO2gqRFXM7toZqfLtk2Y\nWYekTjNGH7HUAAAgAElEQVTrM7PnW9JDbBqBKwAAaAfNpgpUZWYXW31ONO/yZenKFX9/1y6pqyve\n/gAAADSr5YErkiU62nrwoNTBMw4AAFKqZo6rc25RkjV5bjOzQ00eixYhTQAAALSLepOz5lQ9cM3J\nl8UqlW3vCb5SVSBmy8ss8woAANpHzcDVzPorbXfOvV9Sl5n1VdiXkTQr6bdb0kM0bXFRunHD37/l\nFmn//nj7AwAAsBnNZjx+XNKXK+0ws5J8uayxZjuF1mCZVwAA0E6aDVx7JdWan35QLPkaO/JbAQBA\nO2k2cH1K0ohz7u3lO5xzR8TKWbG7elV66SV/3zlfUQAAACDNml05a0TSY5IKzrlJ+ZxWSeqWD1rD\nNohJdLS1s1Pasye+vgAAALRCU4Grmc045/oknZZf2jW6vGtB0oiZfbMF/UOTSBMAgO119epVLS4u\n6tKlS7oRzowFdphdu3bptttuU1dXl/bt29fy8zc74iozK0jqdc4dkJQNNhdZOSt+ZgSuALCdrl69\nqh/+8Ifq7OzUkSNHtGfPHjlmxGKHMTNdu3ZNL730kn74wx/qjW98Y8uD16YD11AQqD7Vgr6gRV56\nSXrtNX9/717pwIF4+wMA7W5xcVGdnZ06dIh1d7BzOee0d+/elb+DxcVF/czP/ExLr1E3cHXOvVdS\nxsy+Gtl2ZyMnN7OnN9E3NCk62nroEGWwAGCrXbp0SUeOHIm7G0Bi3H777XrhhRe2P3CVNCXpgKRd\nkW0F1V4K1gX7d9Vogy1CmgAAbK8bN25oD7NggRV79uzZklzvRgLXT2p9zdYPqXbgiphcv+5XzAoR\nuALA9iCnFVi1VX8PdQNXMxuvsG1iS3qDTVtY8JOzJOm226Sbboq3PwAAAK3S7AIESKgXX1y9z2gr\nAABoJzVHXBudhFUNk7O2XzS/9fDh+PoBAADQavVGXAuS5pq4hcdhG12+LF254u/v2uVXzAIAIC4T\nExNyzqlQSNYq8EntF+qrl+PKJKwUiY62Hjzog1cAAFDf+Pi4RkdHNTk5qVwuF3d3UEXNwJVJWOlC\nGSwAAJqzsLCgUqmkUqkUd1dQA5Oz2sTysq8oECJwBQCgcWNjYzIzDQwMxN0V1LDpJV+dc0eq7TOz\nFzZ7fjRmacnXcJWkm2+W9u+Ptz8AAACt1nTg6pwblXSyTjOyLLdJeZoAdbABAEC7aSpwdc59TNKI\n/HKws/Kra01Impf085LeL2msRX1EA8hvBYDk+uhHP6qnn05Xhcg777xTn/3sZ1tyrsXFRY2MjGhm\nZkaFQkG5XE6Tk5PKZDIrbUqlkiYmJpTP51UsFpXNZjU8PKyTJ9eOkRUKhZVzSVI2m9XAwIDGxlbD\njmKxuNKmq6tr3f5KJiYmNDw8rLm5OfX09Kzbls/ndfbsWUla6X+5Zq6LjWl2xHVY0piZnZIk59zH\nJZ01s28Gj89KytQ4Hi109ap08aK/75x06FC8/QEAIKq/v18DAwM6ceKEurq6NDMzo3vuuUdzc6uV\nM8+ePat8Pq+BgQF1d3drfn5eIyMjWlhYWBP83XPPPcpms8rn8yqVSpqfn18JYiUfPPb29kqSTp06\npfn5eY2Pj6tUKimfzzfV/8HBQfX09OjUqVM6c+aMpqamNDIysi5YbvV1UYGZbfgmaVnSb0cez5Y9\nfkjS95s5d1Jvvb29llT/+I9mjz7qb9/+dty9AYCd55lnnom7C4mUz+dNko2Nja3ZPjQ0ZJJsenq6\n5vEDAwPmQxVvenq67nG5XM4k2dLS0sq2sbGxNecJ+zU3N9fQtlwut+YamUzGstnshq+702zk70LS\nrDUQjzVbVaAoqTvyeEbSYORxr6Rsk+fGBpEmAABIsvCj91A4Ulnp4/aou+66S5IfzZR8WoCklXSC\ncqVSSTMzMzp58uSaNISwUsDU1FRT/R8eHl7zOJvNanFxccuvi/WaDVwnJEXrRZyR1O+c+7xz7guS\nhuRXz8IWMyNwBQCkSxjclQefYf5qb2+vOjs7NTIysmZ/NpvV0NCQpqam1N3drc7OTg0PD6/UXp2d\nnZXkFxNwzq3curu7K15vo/2tZquui/WazXHNS1oKH5jZU865D0n6YrCpIOn4JvuGBly65HNcJWnP\nHunAgXj7AwBAI8qDwXAiVC6X0/DwsLLZrCYnJzUxsXYtpHw+r+Hh4ZXJUhMTE5qdnV2TLzs2NlZx\n9atwxHarxHXdnaRm4OqcO2IVarGa2UVJp8u2TUiacM4dCPZjG7z44up9ymABANIgXKEqmkIwPDys\noaGhNROZCoXKH9729PQon88rn89rfHxcIyMjKhQK6uvrW9Nmu8R13Z2oXqpA0Tn39865/9s5d3sj\nJyRo3V6kCQAAkq78o/LR0VFJ0okTJySp6jKr586dW/O4VCqtO1cYKC4uLiqTyWhgYECjo6PrzrmV\ny7nGdd2dqF6qwGn5SVen5UdTpyVNSpo0s5e2unOo7fp1KZIbTuAKAEikMFc1+vH/wMDAStCZyWTU\n09OzkhbQ29ur6enpdZOaZmZmNDg4qFwut6bWaiaTWfmI/vTp07rjjjt0xx13aGhoSAcPHtT8/Lwm\nJiY0OTm5ZUu6xnXdnabmiKuZDZtZl6R7Jf0X+cUFTktacs593Tn3a9vQR1SxsOAnZ0nSbbdJN90U\nb38AAKhkbGxM09PTGhwcXJl9X15RYHJyciV4HRsbU1dX17rFBwYGBpTP57W4uKjx8XFNTEyor69v\nTX5rJpPR888/r+PHj6/UW52dnd3y4DGu6+40zsLIp9EDnMvJj8I+FGwy+VHYvJn9v63tXnL09fVZ\nOGswKf7hH6QXXvD3s1nprW+NtTsAsGM9++yzestb3hJ3N4BE2cjfhXNuzsz66rXbcDksM5sJRmI7\n5EdivypfQeBx59wN59wXnHPv3eh5sXHktwIAgJ2k2TquklaC2MFIEHtaUr+kaefchVZ0EJVduSJd\nvuzvd3RIXV3x9gcAAGCrNVvHtZK/l3S7pC75VbM6W3hulImOth48KO3aFV9fAAAAtsOmAtegRNZx\nScOSwsJlTn4J2Hy147B55fVbAQAA2t2GA9cKwWpY8n5GfpLWWWq5bq3lZV9RIHT4cHx9AQAA2C4N\nBa6RYHVQUriWmZNf2jUvgtVttbTka7hK0s03S/v3x9sfAACA7VBvydff1vpgtSgfrE6Z2fNb2z1U\nUl5NgGVeAQDATlBvxHUi+FqUNCVfq5VgNWaUwQIAADtRvcB1XNIZM3tqOzqD+l57TboYJGU4Jx06\nFG9/AAAAtkvNwNXMPr5dHUFjoqOtmYy0Z098fQEAANhOm1qAANuPNAEAALBTtXIBgqY55zKSTkma\nDzZ1m9lIE8eHMpLGzKxYp502cp24mRG4AgCAnSsRgat8/dfhMNB0zmWdc9Nm1l/vwDAYjQagwbZJ\n51z0nBlJc5IGzawQbBto9DpJcOmSdPWqv79nj08VAAAA2CliTxVwzg1JKkVHR4P7JefcQAOnOCVp\nNLrBzEqSRuQXSQiNSSqEQWvQbkpSV4PXiV10tPXQIcpgAQCAnSX2wFU+uJyusP2c1gae1WQldVXY\nXpJPGQgdr3KdWUknGrhO7EgTAABAmpqaUmdnpzo7OzUzM1O13fj4uKampraxZ9hqSUgV6JGvE1uu\nKKmvgeOL8mkB9wQjraEBBYFqkCaQkbRY4fg5+dHYRLt+XVqM9J7AFQCwExWLRQ0ODmpoaEiLi4vq\n7+/X0tKSMhXy586cOaO5ubkYeomtEuuIaxBQSpUDyvIR04qC3NaspOedc7ngvD3BvkbfZiU+W3Rh\nQVpe9vdvu80v9QoAQFoMDg7KOSfnnPr7+1Uqlda1KZVKa9r19vaqWFw7tjU25sea8vm8Tp8+LUka\nHR1dd67x8XENDzfywS3SJAmpAjVFgtta7pAfeZ12zk1L6jKz8XBnMBJbUuWUgu5q13HODTnnZp1z\nsz+Nfk4fA9IEAABp1dvbq5mZGY2NjWlyclLFYlF33HHHuuA12i6fz6tUKqm3t3dNu9nZWWWzWUla\nGWUtFAprzlMqlXTmzBkNDQ1t8XeG7RZ3qkClQLJSm/VvyyLMrOScOyOfrzokKeuc6y8rhzUqqV+r\ny9iGstWuY2YTYfu+vj5roK9bhsAVAJBG4+PjKhQKmpubU09PjySpp6dH3d3dGh0dXRlBnZiYULFY\nXNPu+PHj6uzs1MjIiPL5vCQflEbTArLZrBYX135wOzo6qlOn1lS/RJtI/IirKqcRrHDOZZxzeTMb\nN7NhSb3BrvkwZUCSghHYTHRbUE3gXCPXidOVK9Lly/5+R4fU1Ui4DwBAAoyOjqqnp2clGJV8sJnL\n5TQxsTqWNDk5qWw2u6ZdJpPRwMDAmnbZbHbNCOzi4uLKCKzkA9uZmRkNDKSiYBA2KO7ANQwWq4Zi\nZROuKpmUL30Vti+YWbekqWBf9Fz98qOxQ0EZrhlJB+XLcdW7Tmyio60HD0q7dsXXFwAAGlUqlVQq\nldTXt36udU9Pz8p+SZqZmVkTtIbuuusuSVrJde3p6Vm5Hx4ftpGkkZGRlVFctJ9YA9dI7mmlPNas\n6qQIBHmpFYNOMxsM2mTLtk+Z2URwC6892+S3sC1IEwCA9HvPe96z7vb5z39eknTlypWK+x955BFJ\n0oULFyruP3PmjCTpRz/6UcX9jz76qCTpueeeq7g/LCX19NNPr9u31Q4ePCjJ56yGoiOn5dvCYDVM\nAZiYmNDo6KgymcxKLmupVNLs7Kxyudym+lYqlTQ+Pq7u7m4559Td3a3x8fGq7YeHh9e0HRlZvyhn\nrTYTExNyzq0r7dXb26vOzs4128K2xWJRU1NT6u3t1eDgYEv7PTIyIufcutzhan3aTnHnuEo+aFz/\nm+oDyurF2bysan/EX+94ScqpsXqxsVheli5cWH1M4AoASIswF7U8B1WS5uf9Ku/RUdda5wjbZDIZ\nzc3N6aGHHlJXV5cef/zxlTatGm09e/as8vm8BgYG1N3drfn5eY2MjGhhYWHN+cPJY8ViUUNDQytt\nx8fH1d/fr1wu11CbZoyMjGhqako9PT06ceJES/s9PDys8fFxjY6OanJy9cPrYrGoQqGgkydPNvmT\nbQEzi/UmP5lqssL2aUkDDRw/V2PfdOR+vvw68kFr1eOjt97eXovDwoLZo4/62/S02fJyLN0AANTw\nzDPPxN2FxOrp6TFJtrS0tGZ7JpMxSTY5OWnz8/MmycbGxtYdPz09vdKulvn5ecvlcmuOy2Qylslk\n6h7biIGBAfNh06qhoSGTZHNzc2u2R7/XRtrk83mTZNPT02va9PT0WCaTWbMtbNvIz2Qz/c7lcuue\nt7GxMZNk8/Pzda9rtrG/C0mz1kA8FneOazhzPxv9SD+YQNVlZXVYnXNLzrn5slPknXP58vM658a0\nfmGBfGR/Ntg/uMlvYUuVpwmwzCsAIE3Cj/YHBwdVKBRUKBTU39+/sj9aIWBhYWHd8bVGY6Oio62l\nUmllNPP48eMaHBxcVw92o8pzbSX/sf3AwMC63Nzo99RIm2YMDQ01NAGt2X6HaQNnz55d2ZbP59XT\n01MxpWO7JCFVQJLukXQqEpR2m1lvhXZFlaUGmNmEcy4XCV7D3/C8rS2HNRJco1+rObWDZW0Sh/xW\nAECaDQwM6OTJkxofH1dvr//Xns/nlc1mNTExoWw2q64a5XLCNINagV6xWFSpVFoJxMIqBKdPn1Ym\nk9HExMRKbdhGFQoFnTlzRjMzMyvnL98vaU0QXukc9do0q9o5W9FvScrlcspkMsrn8xoaGlKxWFSx\nWIx94lsiAlfzk6TWZzKvb1cpmJWZzahOPmuj10iS116Tor9vhw7F1xcAAJo1NjamU6dOqVgsrgSX\nvb29ymQyaxYTqDQZKMyFrTXKV57beu7cuZVzhsdGJ4HVMzExoeHhYeVyOQ0PDyubzWpycnJNWa5Q\nIyPCjY4ab0Sln0er+33q1CmNjIyoWCyuBP1xL+qQiMAVlUUnZWUy0t698fUFAIDNyGQyK0FrOMkn\nGmzmcrl1s+olXyYrm81WDVzDYDf6sXexWFy3SMFGAtfh4WENDQ2tGaEtD6rD64VBciWNtGmlVvU7\nNDQ0pJGREc3MzGhmZmZlFDZOsee4ojrSBAAA7WhwcFCZTGbN7PQTJ06oVCqtGR0Mc2Jr5XJWqiRQ\nvkhBsVhsOC+z2khkpUBvYGBAU1NTFZecDbc10qbStSvt385+S6sLQExPT6tQKGh4OP4iTIy4JpSZ\n9OKLq48PH46vLwAANGtmZkaDg4MrHzFPTU2pWCxqenp6TbtwstDw8LDm5+d18ODBlTqt1ZZvLRQK\na9INQuHjcHnYYrHY8Epa4chwGED39vZqenpaU1NT69qePn1ahUJBvb29GhoaWrnW1NSUxsbG1NPT\n01CbcIGG0dFRST6vd6O5pK3ud2h4eHglHzYRq5E1UnqA2/aXw7p4cbUM1te/bnbjxrZeHgCwAZTD\nqm5pacmGhoYsm82aJMvlcuvKMEUNDQ2tlMoaGBhYV0YrKpfLVSzNtLS0ZJLs5MmTKyWcal2z3Pz8\n/EoZr2w2a0NDQ3by5MmKpaDKv7+enp51Zb0aaROWrcpkMis/o1wuV7UcVqXvp9X9DkmyoaGhhn9+\noa0oh+V8W9TT19dnG8mP2az5eenZZ/39n/kZqbfitDQAQBI8++yzestb3hJ3N3aUQqGgfD5ftVJA\nOFFJkk6ePBn7bPi0mpmZUX9/v+bm5iouyVvLRv4unHNzZrZ+beAypAokFPmtAABUl81ma5a3Ghoa\n0vHjx7W4uBhr3dG0C0uXbTRo3SoErgl044YUXR2PwBUAgLUamd2eyWRinwWfRtHyV1NTU2uWfY0b\ngWsCLSxIy8v+/q23SjffHG9/AADAzjIxMaFSqaSTJ08mY1JWgMA1AZaXpccekz73OemJJ6TLl32w\n+nM/J/3H/yj90i9JHRQuAwAA2yCbzWppaSnublRE4Bqz8+elBx7w9++7T/rAB/wo68svS9/5jvSZ\nz0j/+T9Ljz4qHT0ab18BAADiROAao/PnpWPHpAcflHI5ybnVfQcOSPfeK/X3SzMzvt0TTxC8AgCA\nnYvANSbLy36k9cEHfXBajXN+v5n0vvdJzzxD2gAAJJGZyUVHIIAdbKvKrRICxeSxx3xQmss11j4M\nXssWGgEAJEBHR4eWw1m1ALS8vKyOLRhpI3CNyec+51MBGn1z7pxv//DDW9svAMDG3XTTTbpy5Urc\n3QAS48qVK7p5C8oiEbjG5IknpHe+c2PHvOtd0re/vTX9AQA079Zbb1WpVNqyj0eBNDEzlUol7d+/\nv+XnJnCNyeXLvnrARuzf748DACRLZ2enrl+/rh//+Me6evUqASx2JDPT1atX9eMf/1jXr19XZ2dn\ny6/B5KyY7N/vS14dOND4MZcv++MAAMnS0dGhN7zhDVpcXNQPf/hDXb9+Pe4uAbHYvXu3Dhw4oMOH\nD29JjiuBa0yOHfN1Wu+9t/FjnnxSuvvuresTAKB5u3fv1uHDh3X48OG4uwK0LVIFYvLhD0vf+Iav\nFNAIM9/+Ix/Z2n4BAAAkFYFrTMKR1pmZxtpPT/v6rbVqvgIAALQzUgVi0tHhl3E9dsyPpvb3Vy6N\nFdZu/fKXfSUCFh8AAAA7FYFrjI4e9cHoAw/4NIB77/Ulr8LqAU8+ubpQAcu9AgCAnY7ANWZHj0rP\nPutHVR9+WPrSl1arB9x9t1+ooL+fkVYAAAAC1wTo6JDuu8/fAAAAUBnjeAAAAEgFAlcAAACkAoEr\nAAAAUsGxnnJjnHM/lfSDbbrcIUkXtulaaB2et/ThOUsnnrf04TlLp+183v61mb2uXiMC1wRyzs2a\nWV/c/cDG8LylD89ZOvG8pQ/PWTol8XkjVQAAAACpQOAKAACAVCBwTaaJuDuApvC8pQ/PWTrxvKUP\nz1k6Je55I8cVAAAAqcCIKwAAAFKBwBUAAACpsDvuDsBzzmUknZI0H2zqNrORGLuEBjjneiSdkJSR\nlJVUlDRiZqVYO4aGOeeGJBXNbCbuvqC24HVyLHhYkrQgaYK/t2SK/F8LZSSNmVkxpi6hAufcgKQT\nZjZYZX+i4hMC1+SYlDQc/kE757LOuWkz64+5X6giCFr7on/AzrkxSc875+7gn2nyBS/IeUkVX7CR\nHM65rPzr5GDkdXJaUrek4Tj7hvXCYKfs9TEjadI5N0zwGq/Im8CSVgdeqklUfEKqQAIEIz6l6B9y\ncL8UvBNCMuXMbM2My+BFelHS6Xi6hA06Lv/CjeSblDRaFvDkg+1InlOSRqMbgjfzI+KNRuzMrGRm\nw8H/rPlq7ZIYnxC4JsOwpOkK28+JP/AkO1XlD3dKUm67O4ONcc7lJJEekALB31nGzKai281sihSP\nxMpK6qqwPRzhQzokLj4hcE2GHvncyHJFSYlaag3rVPp4ZUG8MCda8DFZho8rU2NY/g0h0qMonxZQ\n/lo4oMqBEJIpcfEJOa4xi/xRL1bYzTvTBDOzziq77lLlP3Qkx/HyNA8kWk4+CMrJv1lclP87GyWX\nPJnMbMQ5tySf8z9oZjPBvACVj5wjmZIanzDimgIV3rEi2XLyuXdIoOCf52zc/UBjIq9/3ZIWzWwi\nCHxGJc3x+phod8i/iZ8OJtJ1mdl4zH1CC8Xx90fgGr9KOUDNtEECBFUFirw4J1qfmRXi7gQaFr7+\nZaPPWzDSOqPV8lhImOA5OiO/bGhOUj6oDoF0SGR8QuCaDpWG6ZEwwUjegKR74u4LKnPODZEikFrn\nKmybk68MgYRxzmWcc3kzGzezYUm9wa75MGUAbWHb4xMC1/iFT3rVdy3kcKXGmKR+nq9kCkZ6yD1O\nn/A1stJztygpQ7pAIk3Kl76SJJlZwcy65SfZUcIsHRIZnzA5K2ZmVnLOVUtyzooak6ngnFtToBmJ\nlJPU65wrX2wgI2nYOdcvaZqJI8kSeY1M1D9PVBe8kShVel7MbNA5N++cy/J6mWxJjU8IXJNhVpXL\nKmVEncnEC/Ja1xRGd871kEeZLNVSBIIC23kC1kSblZ+cVa5LvLlPorDyQzX8X0uPxMUnpAokw6R8\naZdy/fKJ7UioIOg5UyFIZQECoHXy8vnj5frlJ/4gQYLXw1o1PhltTY/ExScErgkQjARlo7Mtg+T1\nLkaBkiuoKdkt/9wNRG5DqvyHjuSickeCBa+DxehKdcFrZE+wZCWSJ++cW1cWMPiEikoQKZHE+MSZ\nWRzXRZkgJ+iUVtcM7uYFObmC52upRpOJYCYtEir4p9onvzJMWFpphJGg5Aqes2jO3Qj5rckVvLkP\nc8rD5ynP31gyBH9PWfnXwfCj/6L8c1SItEtUfELgCgAAgFQgVQAAAACpQOAKAACAVCBwBQAAQCoQ\nuAIAACAVCFwBAACQCgSuAAAASAUCVwAAAKQCgSuAtuOcG3LOzTnnzDm35JybDlY028z5LFgxBoHg\n51prIY5EcM5Nx90HAK1B4AqgrQRBSrjU5Liks/Krw+Q3E7yiMc65k8GbhVzM/ciEb17Ekr5A29gd\ndwcAoFWCwDQnvxToeNm+AfnlDLG1DsovH5mp13ArBUvB9jrnJiUtxtkXAK3DiCuAdjIoqVQetEqS\nmU1F19/G1jCzETNzZjYVd18COUmkCgBtgsAVQDvJitE1BIKc5Iykmbj7AqA1CFwBtJMZSdkgLQDI\nSSoGaQMA2gCBK4B2MhJ8nXTOTdaaIBSpFJAr2z5XY6Z8l3NuLFKxYNo5lyk7vifYbsFt3jk3Vum6\nwUSmWufKBG3mI+c6WeN7ype1HSvbnw1+LkuV9tcS9Dfs61y1NweVKjBEtwU/v6Xg8WTwPWYjP7P5\nZifRBd//WHDLSeoXo61AW2FyFoC2YWYl51yvpElJA5IGnHOSNCXpoRaMvE0H5zojn5KQk/S4pN5I\nm8flJ4ENy39M3R20q3UuBW3mgvah48F5piTNB/vGnHMHzSwM0hUEvHPyqRITkbYnnXPTZjbjnMsG\nbSRpNLI/Y2bDtb7pIAgN80THJS1IOiWpR9JGfqaTkgrB9fvln6NM0O+p4Bqn5CtAzDaakxx8/49L\nGg1za4MAPyef9wygXZgZN27cuLXdTT6oGpMP4kzSkqRMZP9QsD1XdtycpKWybWHbk2Xb89FzyAdK\n685Z5VxDZdun6x0btJv0L90V+9FTtj1T4fzRbSfLz1XlmnNVzj9f42fVU2HbdFnbpWD7QGRb+DMc\n28BzPScpX7YtPE+m0fNw48Yt+TdSBQC0JTMrmJ/h3q3V0c/Tmzxt+QhgOOoZjuqF5baGgxHOWspL\nc4WjnvVGCM9J/mP/yLYhSeuqJlgwwhyMSOYkjdvaUedwdLJqTnBwbE+l82tjo63San3d0GzQz6ny\nbWqwnFbQ9/BNSlS/yG8F2g6BK4C2Z2YT8kFnSydtRYKibPC4KP9R/YCk+SCXM1+eu1rlXGEguybg\njeSFhrm35XmrYS5prZJPfcHXk5HcW5MfMV13zTLhvnP1vocGVAoiNxtYDssHqOVvBHIivxVoOwSu\nAHaKGWndSGUrrAm8zOeL9soHsJIfDX28mRMHk5Tm5EcU8/KjsRNVmjcyQjkS9K38Vu2cadCnygFq\nj1ZzegG0CSZnAWgbwUSjaiN40VHRll1PPmAs/4i+ID8SOBxMEhpzzvVU+Kg9eq4woI62yUuasMjk\nqehs/fBawQS0u2p0Nfz4XbX6UEX48+qu2SoGkZ//fNn2cDJc+Gal5s8eQHow4gqgnTxfqZRSkAc5\noCCns0wm0i7M56ymfLT2VPD1THh8hRHdMGDqKttefp0w/3PlXFX6UClAnZKvoLDmnEF/eoJgfkrS\nqSolt6qO1gbHFiQNRb+3Bn5WWy7yJqX8zUi4glox6GerR9kBxIQRVwDtpChfSmlMfpSxKP9Rck9w\n/6FI23AU8lQwYtml1clW1YwFbYvywVH5pKicfA3ZGa0GrEPyQVT5x9ljzrlu+dHCYQUlocJzmS/t\nFQP98WgAAAE2SURBVAaMkv/YOywhVe6h4Hucc85NyKcvZIO2I0FfHpL0vHxwPyFf0qo76N+gKgf1\noRH5HNq5yLE1S2htoyn5YD460axLqyuoDSndqRAAIhhxBdA2zKxXPgibkQ/chuRHVMfNrDuaRhAE\niFPyAd/p4Ljw2GpG5IPHSa3O0l+pAhDMjh+WD5xOBtef1do6r6HwuFNB+zXnirQpBOcZkQ/Gxit8\n3yWt5qrmgmtnJY2Y2XikzR2SzsoHtGPyQf1g2az+dYKgu18+YD8Z3A/7FreHJIULNZyUn6g1KKkQ\neUxlAaBNODOLuw8AsGMEqQx5Sf0VRmEBADUw4goAAIBUIHAFAABAKhC4AgAAIBXIcQUAAEAqMOIK\nAACAVCBwBQAAQCoQuAIAACAVCFwBAACQCgSuAAAASIX/H5DjwLefpECAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d3e2690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_perf_dim_90(cnn_testing_agnew, logx=False, saveas='cnn_testing_agnew', d_Int=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dir_perf: 1.0\n",
      "d_int=0.0, acc=0.9235\n"
     ]
    },
    {
     "data": {
      "image/png": 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FpMYlfUNSycxmFXJuJalXIbiN+2APuFdv7nDiRHZjAQAAyFpTAa67L5jZgKTT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\nCgEuAAAAugoBLgAAALoKAS4AAAC6yv8P/X8P6bEJ0yEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d821910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_perf_dim_90(cnn_training_agnew, logx=False, saveas='cnn_training_agnew', d_Int=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tISIiKtOspxn/kMFJG3nzTSCdZv4JERF5ViNDPAAAEVlRqUwp9fNGj08t8L3v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\ngAwETRzPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c799c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sub_dim_we(swem_training_agnew, cnn_training_agnew, logx=False, ymin=0.9, saveas='training_agnew')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xYIHkPJSTA1PM229XHsQcOwa8+GL2ttw1OAo5//zZQOTFFysbyRSJAL/3e/K7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\nhoEHERERNcz/D1RTYKmybzsXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d79bb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sub_dim_we(swem_testing_agnew, cnn_testing_agnew, logx=False, ymin=0.88, saveas='testing_agnew')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
